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doi: 10.18178/ijesd.2020.11.9.1288
A Comparative Analysis of Machine Learning Algorithms Modeled from Machine Vision-Based Lettuce Growth Stage Classification in Smart Aquaponics
Abstract— The arising problem on food scarcity drives the innovation of urban farming. One of the methods in urban farming is the smart aquaponics. However, for a smart aquaponics to yield crops successfully, it needs intensive monitoring, control, and automation. An efficient way of implementing this is the utilization of vision systems and machine learning algorithms to optimize the capabilities of the farming technique. To realize this, a comparative analysis of three machine learning estimators: Logistic Regression (LR), K-Nearest Neighbor (KNN), and Linear Support Vector Machine (L-SVM) was conducted. This was done by modeling each algorithm from the machine vision-feature extracted images of lettuce which were raised in a smart aquaponics setup. Each of the model was optimized to increase cross and hold-out validations. The results showed that KNN having the tuned hyperparameters of n_neighbors=24, weights='distance', algorithm='auto', leaf_size = 10 was the most effective model for the given dataset, yielding a cross-validation mean accuracy of 87.06% and a classification accuracy of 91.67%.
Index Terms— Growth stage classification, k nearest neighbors logistic regression, machine learning, machine vision, smart aquaponics, support vector machine.
The authors are with Gokongwei College of Engineering, De La Salle University, Manila, Philippines (e-mail: sandy_lauguico@dlsu.edu.ph).
Cite: Sandy C. Lauguico, Ronnie S. Concepcion II, Jonnel D. Alejandrino, Rogelio Ruzcko Tobias, Dailyne D. Macasaet, and Elmer P. Dadios, " A Comparative Analysis of Machine Learning Algorithms Modeled from Machine Vision-Based Lettuce Growth Stage Classification in Smart Aquaponics," International Journal of Environmental Science and Development vol. 11, no. 9, pp. 442-449, 2020.
Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
